With the growth in popularity of digital imaging, users often amass large collections of digital media assets, including digital still images and digital videos. One of the primary challenges with accessing digital media assets from media collections relates to processing linguistic-based queries against non-linguistic data. Many of the solutions that have been proposed to address this challenge focus on extracting semantic or numeric metadata from the digital media assets and then processing queries against such metadata. For example, algorithms exist to automatically index a collection by semantic concepts such as location (e.g., beach, urban), event type (e.g., vacation, party), and the presence and identity of people. While adding more indexers can increase the potential for a system to answer a wider variety of queries, there are drawbacks to running too many indexers. Indexers can be computationally expensive to run and adding too many rarely used indexes can clutter a metadata database rather than enhance it. As more indexers become available, the challenge is to identify what indexers should be run. While some general indexers might be required to answer commonly asked queries, indexers required for less common queries may depend on individual users. For example, a query for a “cat” might be common for a cat owner, but might never be used by someone who does not own a cat.